Dimitra Panagou Assistant Professor at the Aerospace Engineering Department, University of Michigan

Multi-Agent Dynamic Coverage

Dynamic coverage is defined as forcing an agent to sense/cover over time each point of a domain of interest up to a satisfactory level. For agents with sensing functionals defined over finite footprints, this formulation results in algorithms which set them in motion based on how well they sense the surrounding environment. Thus agents are forced to autonomously and continually explore an unknown region (search) so that each point of this region is sensed ("seen") for a prescribed amount of time ("view time"). This requirement is encoded in a coverage metric that expresses the quality of information accumulated over time through the agent’s sensing footprint. The dynamic coverage problem then reduces to deriving the control laws for the motion of the agents so that the associated coverage error is driven to zero. These control laws force the agents to autonomously move towards, and consequently explore, non-searched regions.

We have developed energy-aware decentralized control algorithms for the motion of agents performing coverage tasks, along with collision avoidance guarantees under anisotropic sensing that creates asymmetric interactions among agents. More information is given below.


D. Panagou, D. M. Stipanovic and P. G. Voulgaris "Distributed dynamic coverage and avoidance control under anisotropic sensing", accepted in the IEEE Transactions on Control of Network Systems, vol. 4, no. 4, pp. 850-862, 2017.

Aerial Sensing Networks in 3D Environments: We extend our dynamic coverage control approach to the problem of increased and sustained 360 Situational Awareness in 3D environments. We are developing an Aerial Sensing Network of small UAVs, that are continually and actively exploring the area around a Ground Station by means of Energy-Aware Dynamic Coverage. More specifically: Planning, navigation and control are tailored to the 3D motion constraints of small UAVs, the limitations of data links, cameras and other onboard sensors, as well as on the UAVs' energy (battery life) constraints. 3D coverage metrics capturing visual information gathering and navigation in 3D spaces are adopted, to yield control algorithms that ensure effective 3D visual coverage. The remaining battery life of each agent is taken into account in the global coverage objective so that power-constrained agents are redistributed in space; this allows those agents with longer battery life to explore farther away from the Ground Station, and those with shorter battery life to return safely to the Ground Station. Collision-free trajectories are generated in real-time through novel 3D coordination and collision avoidance algorithms with provable guarantees. We plan to further study the robustness of the developed control strategies against various sources of uncertainty, such as measurement noise and complex cluttered environments.

This research is sponsored by the Automotive Research Center and US Army TARDEC under the project "SQUAD: Situational Awareness and Sustained Survivability through Man/Unmanned Teaming" "http://arc.engin.umich.edu/research/5_A46_Situational_Awareness.html"


W. Bentz, T. Hoang, E. Bayasgalan and D. Panagou "Complete 3-D Dynamic Coverage in Energy-constrained Multi-UAV Sensor Networks", Autonomous Robots, 2018.

Outdoors Flight over the University of Michigan Wave Field

A Human-centric Network of Free-Flying Co-Robots: We envision to ultimately augment astronauts in spacewalk with the Astronet: A Network of Astronautical Free-Flying Co-Robots, such as SPHERES or Astrobees developed by NASA. The Astronet will safely surround the crew member during EVAs, perceive simple human commands (e.g., gestures) and interpret them into predefined tasks, and respond to human commands by redistributing autonomously in space to dynamically and continually improve task conditions in a human-centric way. We link our dynamic coverage control paradigm with model predictive motion planning methods, as well as with: high-level human inputs, such as gestures, mapped to a predefined set of tasks for the Astronet, motion models for the 3D flight of free floating vehicles, sensing models capturing the uncertainty of the on-board sensors of the Astronet, energy consumption models capturing the limited power resources of the Astronet, and models of the anticipated human motion per task.

This research is sponsored by the NASA Space Technology Research Grants Program through an Early Career Faculty Award "https://www.nasa.gov/directorates/spacetech/strg/ecf2016/AstroNet.html"


D. Han and D. Panagou "Distributed Multi-task Formation Control under Parametric Communication Uncertainties", 56th IEEE Conf. on Decision and Control, Melbourne, Australia, December 2017.

W. Bentz and D. Panagou "Bayesian-inferred Flexible Path Generation in Human-Robot Collaborative Networks", Int. Conf. on Robots and Intelligent Systems, Madrid, Spain, October 2018.

Human-Robot (Astrobee) Interaction in a Virtual Reality Environment of the International Space Station

Human-Robot Interaction for Dynamic Coverage via Gesture Following

Bayesian-inferred Human Intention and Flexible Robot Trajectory Generation

Resilient and Safe Multi-Agent Control

We consider resilience AND safety of dynamic multi-agent networks (e.g., multi-vehicle systems) in adversarial environments. Safety is viewed as the guaranteed safe navigation of the agents within the environment while collaborating towards a common task (e.g., data gathering). Multi-agent collaboration in principle requires coordination and negotiation mechanisms among the agents; these mechanisms are implemented using information shared over wireless communication links. However, wireless communication is vulnerable to cyber-attacks.

Resilience is hence viewed as the guaranteed safe accomplishment of the mission, despite the presence of possible adversaries that can send malicious data over compromised communication links. Our goal is to establish robust (resilient) communication structures for the network, as well as estimation (filtering) and control (coordination/negotiation) mechanisms that will allow the multi-agent network to tolerate or mitigate the adversarial effects of malicious data in the communication structure, while still maintaining safety guarantees.

Our premilinary work includes the establishment of k-circulant graphs as a sufficient communication topology for achieving resilient asymptotic consensus, as well as the extension of strong r-robustness graphs to achieve consensus to arbitrary reference values, that can be used in Leader-Follower networks. We are currently working on incorporating finite-time detection of adversaries and Control Barrier Functions for guaranteed safety despite the destabilizing effects of adversarial agents in the network.

This research is sponsored by Automotive Research Center and US Army TARDEC under the project "Adversarially Robust Coordination for Autonomous Multi-Vehicle Systems", and by the Army Research Office (ARO) under Award No W911NF-17-1-0526.


J. Usevitch and D. Panagou "Resilient Leader-Follower Consensus to Arbitrary Reference Values", 2018 American Control Conference, Milwaukee, Wisconsin, June 2018.

J. Usevitch and D. Panagou "r-Robustness and (r,s)-Robustness of Circulant Graphs", 56th IEEE Conf. on Decision and Control, Melbourne, Australia, December 2017.

Multi-Agent Coordination and Deconfliction

We develop distributed coordination algorithms for multi-agent systems while respecting certain safety and performance guarantees. Safety is realized as the collision-free navigation of the agents towards goal locations under restricted sensing and communication capabilities. Performance is primarily realized as the robustness of the derived solutions against agent/communication/sensing failures and malicious behavior.

We address the concurrent satisfaction of multiple objectives (such as collision avoidance, connectivity maintenance and convergence to goal destinations) via controlled set invariance and a novel class of Lyapunov-like Barrier Functions. Our next goal is to extend the methodology to realistic models for a wide class of autonomous vehicles.

One recent development corresponds to safe motion planning and coordination for agents belonging to different classes, namely class-A and class-B. Agents of class-B do not share information with agents of class-A and do not participate in ensuring safety, modeling thus agents with failed sensing/communication systems, agents of higher priority, or moving obstacles with known upper bounded velocity. We propose the notion of semi-cooperative multi-agent coordination: Semi-cooperative coordination is defined as the ad-hoc prioritization and conflict resolution among agents of the same class; more specifically, participation in conflict resolution and collision avoidance for each agent is determined on-the-fly based on whether the agent's motion results in decreasing its distance with respect to its neighbor agents; based on this condition, the agent decides to either ignore its neighbors, or adjust its velocity and avoid the neighbor agent with respect to which the rate of decrease of the pairwise inter-agent distance is maximal. Guarantees on safety and almost global convergence of the agents to their destinations are formally proved.

Agents of Class-A and Class-B (in V-shape formation).


D. Panagou "A Distributed Feedback Motion Planning Protocol for Multiple Unicycle Agents of Different Classes", IEEE Transactions on Automatic Control, vol. 62, no. 3, pp. 1178-1193, March 2017.

D. Panagou, D. M. Stipanovic and P. G. Voulgaris "Distributed coordination control for multi-robot networks using Lyapunov-like barrier functions", IEEE Transactions on Automatic Control, vol. 61, no. 3, pp. 617-632, March 2016.

Trajectory Prediction and Validation in Multi-sUAS Missions: We aim at developing advanced trajectory prediction methods for multi-sUAS missions. Our focus is on (i) adopting appropriate metrics captured through level sets of properly defined barrier functions for mission accomplishment with certain guarantees, and on (ii) designing computationally efficient mechanisms that will unify high-level planning and low-level control under wind and navigation uncertainty via “abstractions” for certain classes of vehicles and missions. The proposed framework will enable fast and accurate trajectory generation in a distributed fashion for multi-vehicle settings, which can be further used in automated decision making for self-separation in complex environments.

This research is supported by the NASA Grant NNX16AH81A.


K. Garg, D. Han and D. Panagou "Robust Semi-Cooperative Multi-Agent Coordination in the Presence of Stochastic Disturbances", 56th IEEE Conf. on Decision and Control, Melbourne, Australia, December 2017.

X. Ma, Z. Jiao, Z. Wang and D. Panagou "3D Decentralized Prioritized Motion Planning and Coordination for High-Density Operations of Micro Aerial Vehicles", accepted for publication in the IEEE Transactions on Control Systems Technology, to appear.

X. Ma, Z. Jiao, Z. Wang and D. Panagou "Decentralized Prioritized Motion Planning for Multiple Autonomous UAVs in 3D Polygonal Obstacle Environments", 2016 International Conference on Unmanned Aircraft Systems, Arlington, VA, USA, June 2016

Aircraft (Fixed-Wing) Deconfliction via Hybrid Control.

From High-Level Task Specifications to Low-Level Geometric Control via Lyapunov Abstractions: The goal of this research project is to narrow the existing gap between high-level discrete task planning and low-level continuous control in complex multi-agent missions within a control-theoretic framework. We introduce the concept of a Lyapunov abstraction to enable the definition of a consistent mapping between high-level specifications and low-level control commands. A Lyapunov abstraction serves as a system model that by construction satisfies both the low-level dynamics and the high-level goals, i.e., that captures the dynamics, tasks and interactions of a single agent with its environment, and is used as the unit element in the bottom-up composition of a hybrid system encoding the multi-agent mission. The Lyapunov abstractions we propose here are composed of high-level Lyapunov-like barrier functions (barriers) capturing high-level specifications and interactions among agents, and low-level geometric flows capturing feasible system trajectories. The main idea lies on the pairing of a Lyapunov-like barrier function and a geometric flow using notions and tools from geometric control and dynamical systems theory. The proposed method offers a reactive motion planning, decision-making and control design mechanism that is scalable with the number of agents and tasks, and thus applicable to large-scale systems involving hundreds of agents.

This research is sponsored by the Air Force Office of Scientific Research through an Young Investigator Award "http://www.wpafb.af.mil/News/Article-Display/Article/969772/afosr-awards-grants-to-58-scientists-and-engineers-through-its-young-investigat/"

Earlier Work on Multi-Robot Systems

Decentralized Goal Assignment and Trajectory Generation via Multiple Lyapunov Functions | Collaborative work with UPenn: We develop decentralized feedback control policies and coordination protocols for multi-robot systems with certain safety and performance guarantees. Safety is realized as the collision-free motion towards goal locations under restricted sensing and communication capabilities, while performance is realized as the assignment of goals which result in shortest total distance to the goal locations. The formulation within a Multiple Lyapunov-like Barrier Functions approach enables scalable and correct-by-construction algorithms, which perform well for hundreds of agents.


D. Panagou, M. Turpin and V. Kumar "Decentralized goal assignment and trajectory generation in multi-robot networks: A multiple Lyapunov functions approach", 2014 IEEE Int. Conf. on Robotics and Automation, Hong Kong, China, June 2014

6 Agents

100 Agents

Visibility Maintenance for Leader-Follower Formations in Obstacle Environments: We consider GPS-denied obstacle environments where multiple robots need to coordinate their motion using vision-based sensing systems only, in the absence of explicit information exchange. Physical obstacles may obstruct visibility, therefore effective sensing, and furthermore should always be avoided.

We develop decentralized motion coordination algorithms for formations of mobile robots in such constrained environments, which guarantee the collision-free motion of the robotic network and the maintenance of visibility among robotic agents.


D. Panagou and V. Kumar "Cooperative visibility maintenance for leader-follower formations in obstacle environments", IEEE Transactions on Robotics, vol. 30, no. 4, pp. 831-844, Aug. 2014

Dynamic Positioning and Formation Control for Underactuated Marine Vehicles: Guidance, navigation and control of marine vehicles (ships, surface vessels and underwater vehicles) is an active research topic, motivated in part by the extensive use of autonomous vehicles in oil industry, scientific explorations (e.g. in oceanographic, archaeological and marine biology research), search and rescue missions, surveillance and inspection tasks, etc. The underwater environment, in particular, poses additional challenges to guidance, navigation and control tasks due to the lack of GPS measurements. Thus, vision is often the main means of sensing and localization with respect to targets of interest.

We develop hybrid and switching control algorithms for underactuated vehicles which move in the presence of unknown external disturbances and vision-based constraints, which guarantee the practical stability of the system with respect to a target of interest. Trade-offs between visibility maintenance and accurate positioning are studied and analyzed. We also consider multi-vehicle formation control so that multiple marine vehicles maintain visibility with, and eventually encircle, a target of interest.

Our next goals include the extension of the methodologies to docking and collaborative control problems for satellites and spacecraft.


D. Panagou and K. J. Kyriakopoulos "Viability control for a class of underactuated systems", Automatica, 49 (2013), pp. 17-29

D. Panagou and K. J. Kyriakopoulos "Dynamic positioning for an underactuated marine vehicle using hybrid control", International Journal of Control, 2013, http://dx.doi.org/10.1080/00207179.2013.828853

D. Panagou and K. J. Kyriakopoulos "Cooperative formation control of underactuated marine vehicles for target surveillance under sensing and communication constraints", 2013 IEEE Int. Conf. on Robotics and Automation, Karlsruhe, Germany, May 2013